{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-01-13T07:45:41.953000900Z",
     "start_time": "2024-01-13T07:45:36.496208600Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from keras.datasets import cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "(train_images,train_labels),(test_images,test_labels) = cifar10.load_data()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-13T08:31:23.491038200Z",
     "start_time": "2024-01-13T08:31:22.461241700Z"
    }
   },
   "id": "4e8c4ecd099e854c"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(50000, 32, 32, 3)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-13T08:31:35.583307300Z",
     "start_time": "2024-01-13T08:31:35.522321800Z"
    }
   },
   "id": "25e6616cbb5dbe0a"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[[[ 59,  62,  63],\n         [ 43,  46,  45],\n         [ 50,  48,  43],\n         ...,\n         [158, 132, 108],\n         [152, 125, 102],\n         [148, 124, 103]],\n\n        [[ 16,  20,  20],\n         [  0,   0,   0],\n         [ 18,   8,   0],\n         ...,\n         [123,  88,  55],\n         [119,  83,  50],\n         [122,  87,  57]],\n\n        [[ 25,  24,  21],\n         [ 16,   7,   0],\n         [ 49,  27,   8],\n         ...,\n         [118,  84,  50],\n         [120,  84,  50],\n         [109,  73,  42]],\n\n        ...,\n\n        [[208, 170,  96],\n         [201, 153,  34],\n         [198, 161,  26],\n         ...,\n         [160, 133,  70],\n         [ 56,  31,   7],\n         [ 53,  34,  20]],\n\n        [[180, 139,  96],\n         [173, 123,  42],\n         [186, 144,  30],\n         ...,\n         [184, 148,  94],\n         [ 97,  62,  34],\n         [ 83,  53,  34]],\n\n        [[177, 144, 116],\n         [168, 129,  94],\n         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120],\n         [131, 121, 131]],\n\n        [[163, 148, 120],\n         [158, 148, 122],\n         [163, 156, 133],\n         ...,\n         [143, 133, 139],\n         [143, 134, 142],\n         [143, 133, 144]]],\n\n\n       [[[255, 255, 255],\n         [253, 253, 253],\n         [253, 253, 253],\n         ...,\n         [253, 253, 253],\n         [253, 253, 253],\n         [253, 253, 253]],\n\n        [[255, 255, 255],\n         [255, 255, 255],\n         [255, 255, 255],\n         ...,\n         [255, 255, 255],\n         [255, 255, 255],\n         [255, 255, 255]],\n\n        [[255, 255, 255],\n         [254, 254, 254],\n         [254, 254, 254],\n         ...,\n         [254, 254, 254],\n         [254, 254, 254],\n         [254, 254, 254]],\n\n        ...,\n\n        [[113, 120, 112],\n         [111, 118, 111],\n         [105, 112, 106],\n         ...,\n         [ 72,  81,  80],\n         [ 72,  80,  79],\n         [ 72,  80,  79]],\n\n        [[111, 118, 110],\n         [104, 111, 104],\n         [ 99, 106,  98],\n         ...,\n         [ 68,  75,  73],\n         [ 70,  76,  75],\n         [ 78,  84,  82]],\n\n        [[106, 113, 105],\n         [ 99, 106,  98],\n         [ 95, 102,  94],\n         ...,\n         [ 78,  85,  83],\n         [ 79,  85,  83],\n         [ 80,  86,  84]]],\n\n\n       ...,\n\n\n       [[[ 35, 178, 235],\n         [ 40, 176, 239],\n         [ 42, 176, 241],\n         ...,\n         [ 99, 177, 219],\n         [ 79, 147, 197],\n         [ 89, 148, 189]],\n\n        [[ 57, 182, 234],\n         [ 44, 184, 250],\n         [ 50, 183, 240],\n         ...,\n         [156, 182, 200],\n         [141, 177, 206],\n         [116, 149, 175]],\n\n        [[ 98, 197, 237],\n         [ 64, 189, 252],\n         [ 69, 192, 245],\n         ...,\n         [188, 195, 206],\n         [119, 135, 147],\n         [ 61,  79,  90]],\n\n        ...,\n\n        [[ 73,  79,  77],\n         [ 53,  63,  68],\n         [ 54,  68,  80],\n         ...,\n         [ 17,  40,  64],\n         [ 21,  36,  51],\n         [ 33,  48,  49]],\n\n        [[ 61,  68,  75],\n         [ 55,  70,  86],\n         [ 57,  79, 103],\n         ...,\n         [ 24,  48,  72],\n         [ 17,  35,  53],\n         [  7,  23,  32]],\n\n        [[ 44,  56,  73],\n         [ 46,  66,  88],\n         [ 49,  77, 105],\n         ...,\n         [ 27,  52,  77],\n         [ 21,  43,  66],\n         [ 12,  31,  50]]],\n\n\n       [[[189, 211, 240],\n         [186, 208, 236],\n         [185, 207, 235],\n         ...,\n         [175, 195, 224],\n         [172, 194, 222],\n         [169, 194, 220]],\n\n        [[194, 210, 239],\n         [191, 207, 236],\n         [190, 206, 235],\n         ...,\n         [173, 192, 220],\n         [171, 191, 218],\n         [167, 190, 216]],\n\n        [[208, 219, 244],\n         [205, 216, 240],\n         [204, 215, 239],\n         ...,\n         [175, 191, 217],\n         [172, 190, 216],\n         [169, 191, 215]],\n\n        ...,\n\n        [[207, 199, 181],\n         [203, 195, 175],\n         [203, 196, 173],\n         ...,\n         [135, 132, 127],\n         [162, 158, 150],\n         [168, 163, 151]],\n\n        [[198, 190, 170],\n         [189, 181, 159],\n         [180, 172, 147],\n         ...,\n         [178, 171, 160],\n         [175, 169, 156],\n         [175, 169, 154]],\n\n        [[198, 189, 173],\n         [189, 181, 162],\n         [178, 170, 149],\n         ...,\n         [195, 184, 169],\n         [196, 189, 171],\n         [195, 190, 171]]],\n\n\n       [[[229, 229, 239],\n         [236, 237, 247],\n         [234, 236, 247],\n         ...,\n         [217, 219, 233],\n         [221, 223, 234],\n         [222, 223, 233]],\n\n        [[222, 221, 229],\n         [239, 239, 249],\n         [233, 234, 246],\n         ...,\n         [223, 223, 236],\n         [227, 228, 238],\n         [210, 211, 220]],\n\n        [[213, 206, 211],\n         [234, 232, 239],\n         [231, 233, 244],\n         ...,\n         [220, 220, 232],\n         [220, 219, 232],\n         [202, 203, 215]],\n\n        ...,\n\n        [[150, 143, 135],\n         [140, 135, 127],\n         [132, 127, 120],\n         ...,\n         [224, 222, 218],\n         [230, 228, 225],\n         [241, 241, 238]],\n\n        [[137, 132, 126],\n         [130, 127, 120],\n         [125, 121, 115],\n         ...,\n         [181, 180, 178],\n         [202, 201, 198],\n         [212, 211, 207]],\n\n        [[122, 119, 114],\n         [118, 116, 110],\n         [120, 116, 111],\n         ...,\n         [179, 177, 173],\n         [164, 164, 162],\n         [163, 163, 161]]]], dtype=uint8)"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-13T08:31:48.615992Z",
     "start_time": "2024-01-13T08:31:48.595985900Z"
    }
   },
   "id": "b993f10e0205ab3e"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "(50000, 1)"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-13T08:31:59.371466800Z",
     "start_time": "2024-01-13T08:31:59.283412100Z"
    }
   },
   "id": "f0c32b72f9ff81f"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1b801f8d9a0>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 100x100 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(1,1))\n",
    "plt.imshow(train_images[6])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-13T08:33:12.821593100Z",
     "start_time": "2024-01-13T08:33:12.678677100Z"
    }
   },
   "id": "ad318ba2de015a3"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "538699979e1bbccf"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
